PretendMask

PretendMask / AI-Industry-Intelligence-System

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基于Python的AI行业情报分析系统（新能源、航天、电力等领域）

16
0
85% credibility
Found May 31, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A desktop application that monitors industry news, uses AI to analyze and score each story by sentiment and impact, stores results locally, and sends automated email reports on a schedule.

How It Works

1
🔍 Discovering the Intelligence Tool

You hear about a tool that watches industry news and uses AI to highlight what matters most for your business.

2
💻 Installing the Application

You download and install the application on your computer with just a few clicks.

3
📊 Seeing Your Dashboard

You open the app and see a clean dashboard showing recent industry news that has already been analyzed and scored.

4
⚙️ Connecting Your Services

You enter your AI service account and email settings so the app can analyze news and send you reports.

5
🤖 Running Your First Analysis

You click a button to have the AI search for important news and rate each story by how positive or negative it is for your industry.

6
📈 Exploring Your Results

You browse charts and heatmaps showing which topics are trending, which news sources are most reliable, and how sentiment changes over time.

7
⏰ Setting Up Scheduled Reports

You choose morning, noon, or evening times for the app to automatically run analyses and email you summaries.

📧 Receiving Automated Intelligence

Each day at your chosen time, you receive an email with the most important industry news, complete with scores and explanations.

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Star Growth

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AI-Generated Review

What is AI-Industry-Intelligence-System?

This is a Python desktop application that monitors and analyzes industry news across sectors like new energy, aerospace, and power infrastructure. You configure keywords, set up scheduled analysis times, and the system queries the DeepSeek AI to generate structured intelligence reports with sentiment scores and impact assessments. Reports get stored locally in SQLite and optionally pushed to your inbox via email. The interface includes dashboards, trend charts, and heatmaps so you can visualize patterns over time.

Why is it gaining traction?

The combination of scheduled automation and local storage hits a sweet spot for analysts who want to build proprietary intelligence feeds without relying on third-party SaaS subscriptions. The encrypted credential storage means you can safely store your API keys on disk. Having both manual trigger and scheduled runs (morning, noon, evening) gives flexibility for different workflows. The built-in scoring system categorizes reports as high/medium/low impact, which saves manual triage time.

Who should use this?

Industry analysts tracking新能源 (new energy), grid, or AI sectors who want a personal news intelligence pipeline. Researchers who need to build time-stamped archives of AI-generated summaries for later analysis. Professionals who prefer desktop tools over browser-based services and want their data stored locally rather than in someone else's cloud. Not suitable for teams needing collaborative features or web-based access.

Verdict

At 16 stars with minimal documentation, this is an early-stage personal tool rather than a production-ready platform. The credibility score of 0.85% reflects that maturity gap. If you want a self-hosted intelligence aggregator with AI-powered analysis and are comfortable with a Python desktop app, it's worth a weekend evaluation. Don't bet a production workflow on it yet.

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